A fuzzy associative classification method for multi-class imbalanced datasets is presented. The method
implements a better combination of AdaBoost.M1W and the process of building fuzzy associative classification by the genetic
optimization objective, which is minimization weighted error rate in the process of ensemble iterative learning and the number
of fuzzy association rule and total fuzzy items in the weak fuzzy associative classifier. The experiments of comparing with
existing data preprocessing approaches aiming at the imbalanced classification problem show that the proposed method can
dramatically improve the classification performance of the fuzzy associative classifier for multi-class imbalanced datasets by
five UCI multi-class imbalanced benchmark datasets.